"""MI35x Nightly performance benchmark for GLM-5.

Tests GLM-5 with DSA attention backend using bench_one_batch on 8 GPUs.

Registry: nightly-perf-8-gpu-mi35x-glm5 suite
"""

import os
import unittest
from typing import List

from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.nightly_bench_utils import BenchmarkResult
from sglang.test.nightly_utils import NightlyBenchmarkRunner
from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env

register_amd_ci(est_time=5400, suite="nightly-perf-8-gpu-mi35x-glm5", nightly=True)


def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
    model_header = results[0].model_path
    if results[0].run_name and results[0].run_name != "default":
        model_header += f" ({results[0].run_name})"

    gpu_config = os.getenv("GPU_CONFIG", "MI35x")
    if gpu_config:
        model_header += f" [{gpu_config}]"

    summary = f"### {model_header}\n"
    summary += "| batch size | input len | latency (s) | input throughput (tok/s) | output throughput (tok/s) | ITL (ms) |\n"
    summary += "| ---------- | --------- | ----------- | ------------------------ | ------------------------- | -------- |\n"

    report_results = (
        results[1:]
        if len(results) > 1 and results[0].batch_size == results[1].batch_size
        else results
    )

    for result in report_results:
        itl = 1 / (result.output_throughput / result.batch_size) * 1000
        summary += f"| {result.batch_size} | {result.input_len} | {result.latency:.2f} | {result.input_throughput:.2f} | {result.output_throughput:.2f} | {itl:.2f} |\n"

    return summary


GLM5_MODEL_PATH = os.environ.get("GLM5_MODEL_PATH", "zai-org/GLM-5-FP8")
RESULT_DIR = "performance_results_glm5_mi35x"


class TestGLM5PerfMI35x(unittest.TestCase):
    """Nightly performance benchmark for GLM-5 on MI35x.

    Tests GLM-5 with DSA attention backend on TP=8.
    """

    @classmethod
    def setUpClass(cls):
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.batch_sizes = [1, 8, 16, 64]
        cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
        cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))

        cls.model_config = {
            "name": "glm5-mi35x",
            "model_path": GLM5_MODEL_PATH,
            "other_args": [
                "--trust-remote-code",
                "--reasoning-parser",
                "glm45",
                "--tool-call-parser",
                "glm47",
                "--tp",
                "8",
                "--dsa-prefill-backend",
                "tilelang",
                "--dsa-decode-backend",
                "tilelang",
                "--kv-cache-dtype",
                "fp8_e4m3",
                "--chunked-prefill-size",
                "131072",
                "--mem-fraction-static",
                "0.85",
                "--model-loader-extra-config",
                '{"enable_multithread_load": true, "num_threads": 8}',
                "--watchdog-timeout",
                "1200",
            ],
            "env_vars": {
                "SGLANG_ROCM_FUSED_DECODE_MLA": "0",
                "ROCM_QUICK_REDUCE_QUANTIZATION": "INT4",
                "SAFETENSORS_FAST_GPU": "1",
            },
        }

        os.environ.setdefault("SGLANG_BENCH_TIMEOUT", "3600")
        cls.runner = NightlyBenchmarkRunner(RESULT_DIR, cls.__name__, cls.base_url)
        cls.runner.setup_result_directory()
        cls.runner.full_report = f"## {cls.__name__}\n"

    def test_glm5_perf(self):
        """Run GLM-5 performance benchmark on MI35x."""
        old_env = {}
        for key, value in self.model_config.get("env_vars", {}).items():
            old_env[key] = os.environ.get(key)
            os.environ[key] = value

        try:
            result_tuple = self.runner.run_benchmark_for_model(
                model_path=self.model_config["model_path"],
                batch_sizes=self.batch_sizes,
                input_lens=self.input_lens,
                output_lens=self.output_lens,
                other_args=self.model_config["other_args"],
                variant=self.model_config["name"],
                extra_bench_args=["--trust-remote-code"],
                timeout=5400,
            )
            results = result_tuple[0]
            success = result_tuple[1]

            if results:
                self.runner.full_report += (
                    generate_simple_markdown_report(results) + "\n"
                )

            self.assertTrue(success, f"Benchmark failed for {GLM5_MODEL_PATH} on MI35x")
        finally:
            for key, value in old_env.items():
                if value is None:
                    os.environ.pop(key, None)
                else:
                    os.environ[key] = value
            self.runner.write_final_report()


if __name__ == "__main__":
    unittest.main()
